Cooperators have low degrees
#Define degrees of isolation
isolationDegree = 1
#number of iterations per arm
iterations = 100
modelForPrediction = "random forest" #"linear" or "random forest"
# List of manipulating parameters of experiments
#L : number of rounds
#V : Visible or not
#A : Income of a rich-group subject
#B : Income of a poor-group subject
#R : Probability to be assigned to a rich group
#I : Number of the same-parameter trial
R = 0.5
I = 0
L = 10
trends.df = data.frame()
for(A in c(1150,700,500)){
for(V in c(0,1)){
V = V
A = A
if(A==1150){B = 200} #high inequality
if(A==700){B = 300} #low inequality
if(A==500){B = 500} #no inequality
if(modelForPrediction=="random forest"){
source(paste(rootdir,"R/models.R",sep="/"))
if(V==0){
model1<-model1.invisible(redo=FALSE)
model2<-model2.invisible(redo=FALSE)
model3<-model3(redo=FALSE)
}
if(V==1){
model1<-model1.visible(redo=FALSE)
model2<-model2.visible(redo=FALSE)
model3<-model3(redo=FALSE)
}
}
df.netIntLowDegree = data.frame(
coopFrac = NULL,
avgCoop = NULL,
avgCoopFinal = NULL,
percentIsolation = NULL,
isolation = NULL,
percentIsolationC = NULL,
percentIsolationD = NULL,
nCommunities = NULL,
communitySize = NULL,
assortativityInitial = NULL,
assortativityFinal = NULL,
conversionRate = NULL,
conversionToD = NULL,
conversionToC = NULL,
transitivity = NULL,
degree = NULL,
degreeC = NULL,
degreeD = NULL,
meanConversionToD = NULL,
meanConversionToC = NULL,
degreeLost = NULL,
degreeLostC = NULL,
degreeLostD = NULL
)
#Here, factionCoop=0 will be the control: no rearranging of nodes will take place
for(frac in c(0,0.25,0.5,0.75,1)){
#nodes in the top fractionCoop degrees will automatically be a cooperator
fractionCoop = frac
coopFrac = NULL
avgCoop = NULL
homophilyC = NULL
homophilyD = NULL
heterophily = NULL
avgCoopFinal = NULL
percentIsolation = NULL
isolation = NULL
percentIsolationC = NULL
percentIsolationD = NULL
nCommunities = NULL
communitySize = NULL
assortativityInitial = NULL
assortativityFinal = NULL
conversionRate = NULL
conversionToD = NULL
conversionToC = NULL
transitivity = NULL
degree = NULL
degreeC = NULL
degreeD = NULL
meanConversionToD = NULL
meanConversionToC = NULL
degreeLost = NULL
degreeLostC = NULL
degreeLostD = NULL
avg_wealth = NULL
gini = NULL
for(m in c(1:iterations)){
# Section 1. NOTES, packages, and Parameters
#Importing library
library(igraph) # for network graphing
library(reldist) # for gini calculatio
library(boot) # for inv.logit calculation
#Two prefixed functions
#rank
rank1 = function(x) {rank(x,na.last=NA,ties.method="average")[1]} #a smaller value has a smaller rank.
#gini mean difference (a.k.a. mean difference: please refer to https://stat.ethz.ch/pipermail/r-help/2003-April/032782.html)
gmd = function(x) {
x1 = na.omit(x)
n = length(x1)
tmp = 0
for (i in 1:n) {
for (j in 1:n) {
tmp <- tmp + abs(x1[i]-x1[j])
}
}
answer = tmp/(n*n)
return(answer)
}
# List of fixed parameters of experiments (assumptions)
#Rewiring rate = 0.3
#GINI coefficient (can be known by A or B)
GINI = 0*as.numeric(A==500) + 0.2*as.numeric(A %in% c(700,850)) + 0.4*as.numeric(A ==1150)
#Collecting data frame (final output data frame)
result = data.frame(round=0:L,n_par=NA,n_A=NA,avg_coop=NA,avg_degree=NA,avg_wealth=NA,gini=NA,gmd=NA,avg_coop_A=NA,avg_degree_A=NA,avg_wealth_A=NA,gini_A=NA,gmd_A=NA,avg_coop_B=NA,avg_degree_B=NA,avg_wealth_B=NA,gini_B=NA,gmd_B=NA,isolation=NA,percentIsolation=NA,meanConversionToD=NA,meanConversionToC=NA,degreeLost=NA,degreeLostC=NA,degreeLostD=NA)
#_A is for a richer group and _B is for a poorer group
#####################################################
# Section 1.5: Practice rounds 1 to 2, to determine C/D in round 1
N = 17 # median of the number of participants over rounds.
node_rp0 = data.frame(ego_id=1:N, round=0)
node_import = node_rp0
for (k in 1:2){
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if (k==1) {
node_rX$prob_coop = inv.logit(1.099471)
} else {
node_rX$prob_coop = inv.logit((-0.02339288) + (1.46068980)*as.numeric(node_rX$prev_coop==1))
}
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
node_rX$prev_coop = node_rX$coop
assign(paste("coop_rp",k, sep=""),node_rX$coop)
#For the loop
node_import = node_rX
}
#cooperation rate in the practice rounds
coop_rp = apply(cbind(coop_rp1,coop_rp2),1,mean)
#####################################################
# Section 2: Round 0 (Agents and environments)
#Node data generation
N = 17 # median of the number of participants over rounds.
node_r0 = data.frame(ego_id=1:N, round=0)
node_r0$coop_rp = ifelse(coop_rp==1,"C","D")
node_r0$group = sample(c("rich","poor"),N,replace=TRUE,prob=c(R,1-R)) #R is defined as the probability to be assigned to the rich group
node_r0$initial_wealth = ifelse(node_r0$group=="rich",A,B)
#Link data generation
ego_list = NULL
for (i in 1:N) { ego_list = c(ego_list,rep(i,N)) }
link_r0 = data.frame(ego_id=ego_list,alt_id=rep(1:N,N))
link_r0 = link_r0[(link_r0$ego_id < link_r0$alt_id),] #The link was bidirectional, and thus the half and self are omitted.
link_r0$connected = sample(0:1,dim(link_r0)[1],replace=TRUE,prob=c(0.7,0.3)) #Initial rewiring rate is fixed, 0.3
link_r0c_ego = link_r0[link_r0$connected==1,]
link_r0c_alt = link_r0[link_r0$connected==1,]
colnames(link_r0c_alt) = c("alt_id","ego_id","connected")
link_r0c = rbind(link_r0c_ego,link_r0c_alt) #this is bidirectional (double counted) for connected ties.
link_r0c = link_r0c[order(link_r0c$ego_id),]
link_r0c$alternumber = NA #putting the number for each alter in the same ego
link_r0c[1,]$alternumber = 1
for (i in 1:(dim(link_r0c)[1]-1))
{if (link_r0c[i,]$ego_id == link_r0c[i+1,]$ego_id)
{link_r0c[i+1,]$alternumber = link_r0c[i,]$alternumber + 1}
else
{link_r0c[i+1,]$alternumber = 1}
#print(i)
}
link_r0c2 = reshape(link_r0c, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_r0c2$initial_degree = apply(link_r0c2[,colnames(link_r0c2)[substr(colnames(link_r0c2),1,6) == "alt_id"]],1,function(x){length(na.omit(x))}) #Degree of each ego
link_r0c2[is.na(link_r0c2$initial_degree)==1,"initial_degree"] = 0
#Reflect the degree and initial local gini coefficient into the node data
node_r0 = merge(x=node_r0,y=link_r0c2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_r0$initial_avg_env_wealth = NA
node_r0$initial_local_gini = NA #local gini coefficient of the ego and connecting alters
node_r0$initial_rel_rank = NA #local rank of ego among the ego and connecting alters (divided by the number of the go and connecting alters)
for (i in 1:(dim(node_r0)[1])){
node_r0[i,]$initial_avg_env_wealth = mean(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_local_gini = gini(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_rel_rank = rank1(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))/length(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
}
#Finalization of round 0 and Visualization
#plot(graph.data.frame(link_r0[link_r0$connected==1,],directed=F)) #plot.igraph
node_r0$everIsolated = 0
node_r0$maxDegreeLost = NA
result[result$round==0,2:25] = c(length(node_r0$ego_id),length(node_r0[node_r0$group=="rich",]$ego_id),NA,mean(node_r0$initial_degree),mean(node_r0$initial_wealth),gini(node_r0$initial_wealth),gmd(node_r0$initial_wealth),NA,mean(node_r0[node_r0$group=="rich",]$initial_degree),mean(node_r0[node_r0$group=="rich",]$initial_wealth),gini(node_r0[node_r0$group=="rich",]$initial_wealth),gmd(node_r0[node_r0$group=="rich",]$initial_wealth),NA,mean(node_r0[node_r0$group=="poor",]$initial_degree),mean(node_r0[node_r0$group=="poor",]$initial_wealth),gini(node_r0[node_r0$group=="poor",]$initial_wealth),gmd(node_r0[node_r0$group=="poor",]$initial_wealth),
as.numeric(ifelse(is.na(table(node_r0$initial_degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_r0$everIsolated)/length(node_r0$ego_id)),
NA,
NA,
NA,NA,NA
)
#For the loop at the next round (for round 1, the initial one is the same as the previous [1 prior] one)
node_import = node_r0
node_import$initial_coop = NA
node_import$prev_coop = NA
node_import$prev_wealth = node_import$initial_wealth
node_import$prev_degree = node_import$initial_degree
node_import$prev_avg_env_wealth = node_import$initial_avg_env_wealth
node_import$prev_local_gini = node_import$initial_local_gini
node_import$prev_rel_rank = node_import$initial_rel_rank
node_import$prev_local_rate_coop = NA
link_import = link_r0
#####################################################
# Section 3: Rounds 1 to 10 or more (behaviors in simulation: the equation of cooperation is different at round 1 because of no history)
#3-1: Cooperation phase
for (k in 1:L)
{
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if(modelForPrediction=="linear"){
if (k==1) {
node_rX$prob_coop = as.numeric(V==0)*inv.logit((-1.816665) + (2.086067)*coop_rp1 + (1.800153)*coop_rp2) + as.numeric(V==1)*inv.logit((-2.031577) + (2.427157)*coop_rp1 + (1.684193)*coop_rp2 + (-1.528851)*GINI)
} else {
node_rX$prob_coop = as.numeric(V==0 & node_rX$prev_coop==0)*inv.logit(-1.039916) + as.numeric(V==0 & node_rX$prev_coop==1)*inv.logit(2.062023) + as.numeric(V==1 & node_rX$prev_coop==0)*inv.logit((-0.2574838)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-1.214198)*GINI + (2.508148)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.9749075)) + as.numeric(V==1 & node_rX$prev_coop==1)*inv.logit((- 0.6197254)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.7480261)*GINI + (1.169674)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (1.356784))
}
}
if(modelForPrediction=="random forest"){
if (k==1) {
if(V==1){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2,
gini = GINI
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2
),
type = "prob"
)[[1]]$C}
} else {
if(V==1){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
gini = GINI,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
}
}
#####rearrange node degrees before round 1 depending on cooperation in practice rounds!
if(k==1){
if(fractionCoop==0){
node_rX$prob_coop
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
coop_rp_init = coop_rp
}
if(fractionCoop>0){
prob_coop_df = NULL
nodesCoop = NULL
#nodesCoop = node_rX$prev_degree<=quantile(node_rX$prev_degree,fractionCoop) #assign low-degree nodes to cooperators
#assign defectors to designated nodes
nodesCoop = node_rX$prev_degree<=floor(quantile(node_rX$prev_degree,fractionCoop)) & node_rX$prev_degree>=floor(quantile(node_rX$prev_degree,fractionCoop-0.25))
prob_coop_df =
data.frame(
prob_coop = rev(node_rX$prob_coop[order(coop_rp)]),
node_number = c(which(!nodesCoop),which(nodesCoop))
)
node_rX$prob_coop = prob_coop_df[order(prob_coop_df$node_number),]$prob_coop
#coop_rp of the rearranged nodes
coop_rp_init = rev(coop_rp[order(coop_rp)])[order(prob_coop_df$node_number)]
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
} else {
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
if (k==1) {
node_rX$initial_coop = node_rX$coop
} else {
node_rX$initial_coop = node_rX$initial_coop
}
node_rX$cost = (-50)*node_rX$coop*node_rX$prev_degree
node_rX$n_coop_received = NA
for (i in 1:(dim(node_rX)[1]))
{
node_rX[i,]$n_coop_received = sum(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) ==
"alt_id"]],"coop"])
}
node_rX$benefit = 100*node_rX$n_coop_received
node_rX$payoff = node_rX$cost + node_rX$benefit
node_rX$wealth = node_rX$prev_wealth + node_rX$payoff
node_rX$rel_rank = NA
node_rX$local_rate_coop = NA
for (i in 1:dim(node_rX)[1])
{
node_rX[i,]$rel_rank = rank1(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX[node_rX$ego_id %in%
node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX[i,]$local_rate_coop = mean(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"coop"]))
}
node_rX$growth = as.numeric((node_rX$wealth/node_rX$prev_wealth) > 1)
node_rX = node_rX[,c("ego_id","round","group","prev_degree","initial_wealth","initial_local_gini","initial_coop","coop","wealth","rel_rank","local_rate_coop","growth","everIsolated","maxDegreeLost")] #Pruning the previous-round data (degree is not updating yet)
#3-2: Rewiring phase
# 30% of ties (unidirectional) are being rewired
link_rX_1 = link_import #Importing data (bidirectioanl ego-alter [ego_id < alter_id])
colnames(link_rX_1) = c("ego_id","alt_id","prev_connected")
link_rX_1$challenge = sample(0:1,dim(link_rX_1)[1],replace=TRUE,prob=c(0.7,0.3)) # The bidirectional ties being rewired are selected (rewiring rate = 0.3).
ego_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(ego_node_data) =
c("ego_id","ego_wealth","ego_coop","ego_prev_degree","ego_initial_wealth","ego_initial_local_gini","ego_initial_coop","ego_rel_rank","ego_local_rate_coop","ego_growth")
alt_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(alt_node_data) =
c("alt_id","alt_wealth","alt_coop","alt_prev_degree","alt_initial_wealth","alt_initial_local_gini","alt_initial_coop","alt_rel_rank","alt_local_rate_coop","alt_growth")
link_rX_2 = merge(x=link_rX_1,y=ego_node_data,all.x=TRUE,all.y=FALSE,by="ego_id")
link_rX_3 = merge(x=link_rX_2,y=alt_node_data,all.x=TRUE,all.y=FALSE,by="alt_id")
link_rX_3$choice = sample(c("ego","alt"),dim(link_rX_3)[1],replace=TRUE,prob=c(0.5,0.5)) #decision maker for breaking a link, which is a unilateral decision
#ego_prob: probability of choosing to connect when challenged (asked)
if(modelForPrediction=="linear"){
link_rX_3$ego_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$ego_coop + (2.96549)*link_rX_3$alt_coop + (-0.1808545))
link_rX_3$alt_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$alt_coop + (2.96549)*link_rX_3$ego_coop + (-0.1808545))}
if(modelForPrediction=="random forest"){
link_rX_3$ego_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$ego_coop,
alter_behavior = link_rX_3$alt_coop
),
type = "prob"
)[[1]]$C
link_rX_3$alt_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$alt_coop,
alter_behavior = link_rX_3$ego_coop
),
type = "prob"
)[[1]]$C
}
link_rX_3$prob_connect = ifelse(link_rX_3$prev_connected == 1, ifelse(link_rX_3$choice == "ego", link_rX_3$ego_prob,
link_rX_3$alt_prob), link_rX_3$ego_prob*link_rX_3$alt_prob)
link_rX_3$connect_update = apply(data.frame(link_rX_3$prob_connect),1, function(x) {sample(1:0,1,prob=c(x,(1-x)))})
link_rX_3$connected = ifelse(link_rX_3$challenge==0,link_rX_3$prev_connected,link_rX_3$connect_update)
link_rX = link_rX_3[,c("ego_id","alt_id","connected")] #pruning and data is updated
#Reflect the degree and local gini coefficient into the node data
link_rXc_ego = link_rX[link_rX$connected==1,]
link_rXc_alt = link_rX[link_rX$connected==1,]
colnames(link_rXc_alt) = c("alt_id","ego_id","connected")
link_rXc = rbind(link_rXc_ego,link_rXc_alt)
link_rXc = link_rXc[order(link_rXc$ego_id),]
link_rXc$alternumber = NA
link_rXc[1,]$alternumber = 1
for (i in 1:(dim(link_rXc)[1]-1))
{
if (link_rXc[i,]$ego_id == link_rXc[i+1,]$ego_id)
{
link_rXc[i+1,]$alternumber = link_rXc[i,]$alternumber + 1
}
else
{
link_rXc[i+1,]$alternumber = 1
}
#print(i)
}
link_rXc2 = reshape(link_rXc, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_rXc2$degree = apply(link_rXc2[,colnames(link_rXc2)[substr(colnames(link_rXc2),1,3) == "alt"]],1,function(x) {length(na.omit(x))})
node_rX_final = merge(x=node_rX[,c("ego_id","round","group","initial_wealth","initial_local_gini","initial_coop","coop","wealth","growth","everIsolated","maxDegreeLost")],y=link_rXc2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_rX_final[is.na(node_rX_final$degree)==1,"degree"] = 0
node_rX_final$avg_env_wealth = NA
node_rX_final$local_gini = NA #needs to be updated because the social network changes at the rewiring phase
node_rX_final$local_rate_coop = NA
node_rX_final$rel_rank = NA
for (i in 1:dim(node_rX_final)[1])
{
node_rX_final[i,]$avg_env_wealth = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_gini = gini(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_rate_coop = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"coop"]))
node_rX_final[i,]$rel_rank = rank1(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$everIsolated = ifelse(node_rX_final[i,]$everIsolated==1,1,ifelse(node_rX_final[i,]$degree<=isolationDegree,1,0))
node_rX_final[i,]$maxDegreeLost = pmax(node_r0[i,]$initial_degree - node_rX_final[i,]$degree, node_rX_final[i,]$maxDegreeLost, na.rm=TRUE)
}
#Finalization of round X and Visualization
#plot(graph.data.frame(link_rX[link_rX$connected==1,],directed=F)) #plot.igraph
result[result$round==k,2:25] =
c(length(node_rX_final$ego_id),length(node_rX_final[node_rX_final$group=="rich",]$ego_id),mean(node_rX_final$coop),mean(node_rX_final$degree),mean(node_rX_final$wealth),gini(node_rX_final$wealth),gmd(node_rX_final$wealth),mean(node_rX_final[node_rX_final$group=="rich",]$coop),mean(node_rX_final[node_rX_final$group=="rich",]$degree),mean(node_rX_final[node_rX_final$group=="rich",]$wealth),gini(node_rX_final[node_rX_final$group=="rich",]$wealth),gmd(node_rX_final[node_rX_final$group=="rich",]$wealth),mean(node_rX_final[node_rX_final$group=="poor",]$coop),mean(node_rX_final[node_rX_final$group=="poor",]$degree),mean(node_rX_final[node_rX_final$group=="poor",]$wealth),gini(node_rX_final[node_rX_final$group=="poor",]$wealth),gmd(node_rX_final[node_rX_final$group=="poor",]$wealth),
as.numeric(ifelse(is.na(table(node_rX_final$degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_rX_final$everIsolated)/length(node_rX_final$ego_id)),
prop.table(table(node_rX_final[node_rX_final$initial_coop==1]$coop))["0"],
prop.table(table(node_rX_final[node_rX_final$initial_coop==0]$coop))["1"],
suppressWarnings({mean(node_rX_final$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==1]$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==0]$maxDegreeLost,na.rm=TRUE)})
)
#For the loop
node_import = node_rX_final
colnames(node_import)[colnames(node_import) %in%
c("coop","wealth","growth","degree","avg_env_wealth","local_gini","local_rate_coop","rel_rank")] =
c("prev_coop","prev_wealth","prev_growth","prev_degree","prev_avg_env_wealth","prev_local_gini","prev_local_rate_coop","prev_rel_rank")
link_import = link_rX
#print(paste0("Round ",k," is done."))
}
trends.df = rbind(trends.df,cbind(result[c("round","gini","gmd","avg_wealth","avg_coop","avg_degree")],V,GINI,fractionCoop))
link_rX_final = data.table::melt(setDT(node_rX_final),
measure = patterns('alt_id'),
variable.name = 'linkNumber',
value.name = c('alt_id'))
link_rX_final = data.frame(link_rX_final)[c("ego_id","alt_id")]
link_rX_final = link_rX_final[complete.cases(link_rX_final),]
link_rX_final = data.frame(t(unique(apply(link_rX_final, 1, function(x) sort(x))))) %>% distinct(X1, X2)
node_g_final = data.frame(node_rX_final)[c("ego_id","initial_coop","coop")]
node_g_final$initial_coop = factor(node_g_final$initial_coop)
g_rX_final = graph_from_data_frame(link_rX_final, directed = FALSE, vertices=node_g_final)
g_r0 = graph_from_data_frame(link_r0[link_r0$connected==1,][1:2], directed = FALSE, vertices=node_r0)
E(g_r0)$coopEdgeC = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0)))
E(g_r0)$coopEdgeD = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="D",1,0)))
E(g_r0)$coopEdgeCD = sapply(E(g_r0), function(e) ifelse(sum(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0))==1,1,0))
#C-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyC[m] = sum(E(g_r0)$coopEdgeC) / (table(V(g_r0)$coop_rp)["C"]*(table(V(g_r0)$coop_rp)["C"]-1)/2)
#D-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyD[m] = sum(E(g_r0)$coopEdgeD) / (table(V(g_r0)$coop_rp)["D"]*(table(V(g_r0)$coop_rp)["D"]-1)/2)
#heterophily, defined as number of observed C-D edges out of total possible C-D edges
heterophily[m] = sum(E(g_r0)$coopEdgeCD) / (table(V(g_r0)$coop_rp)["C"]*table(V(g_r0)$coop_rp)["D"])
coopFrac[m] = fractionCoop
avgCoop[m] = prop.table(table(V(g_r0)$coop_rp))["C"]
avgCoopFinal[m] = result[result$round==10,]$avg_coop
percentIsolation[m] = max(result[result$round>=1,]$percentIsolation)
isolation[m] = max(result[result$round>=1,]$isolation)
#percentage of isolation among those who cooperated in both practice rounds
percentIsolationC[m] = sum(node_rX_final[coop_rp_init==1,]$everIsolated)/length(node_rX_final[coop_rp_init==1,]$everIsolated)
#percentage of isolation among those who defected at least once in practice rounds
percentIsolationD[m] = sum(node_rX_final[coop_rp_init<=0.5,]$everIsolated)/length(node_rX_final[coop_rp_init<=0.5,]$everIsolated)
nCommunities[m] = max(membership(cluster_louvain(g_rX_final)),na.rm=TRUE)
communitySize[m] = mean(table(membership(cluster_louvain(g_rX_final))),na.rm=TRUE)
assortativityInitial[m] = assortativity(g_r0, V(g_r0)$coop_rp == "C")
assortativityFinal[m] = assortativity(g_rX_final, V(g_r0)$coop_rp == "C")
conversionRate[m] = prop.table(table(V(g_rX_final)$coop == ifelse(V(g_r0)$coop_rp=="C","1","0")))["FALSE"]
conversionToD[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["0"]
conversionToC[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["1"]
transitivity[m] = mean(transitivity(g_rX_final, type="global"),na.rm=TRUE)
degree[m] = mean(igraph::degree(g_rX_final),na.rm=TRUE)
degreeC[m] = mean(igraph::degree(g_r0)[coop_rp_init==1],na.rm=TRUE)
degreeD[m] = mean(igraph::degree(g_r0)[coop_rp_init<=0.5],na.rm=TRUE)
meanConversionToD[m] = mean(result[result$round>=2,]$meanConversionToD, na.rm=TRUE)
meanConversionToC[m] = mean(result[result$round>=2,]$meanConversionToC, na.rm=TRUE)
degreeLost[m] = result[result$round==10,]$degreeLost
degreeLostC[m] = result[result$round==10,]$degreeLostC
degreeLostD[m] = result[result$round==10,]$degreeLostD
avg_wealth[m] = result[result$round==10,]$avg_wealth
gini[m] = result[result$round==10,]$gini
}
df.netIntLowDegree = rbind(df.netIntLowDegree,
data.frame(
coopFrac = coopFrac,
avgCoop = avgCoop,
avgCoopFinal = avgCoopFinal,
percentIsolation = percentIsolation,
isolation = isolation,
percentIsolationC = percentIsolationC,
percentIsolationD = percentIsolationD,
nCommunities = nCommunities,
communitySize = communitySize,
assortativityInitial = assortativityInitial,
assortativityFinal = assortativityFinal,
conversionRate = conversionRate,
conversionToD = conversionToD,
conversionToC = conversionToC,
homophilyC = homophilyC,
homophilyD = homophilyD,
heterophily = heterophily,
transitivity = transitivity,
degree = degree,
degreeC = degreeC,
degreeD = degreeD,
meanConversionToD = meanConversionToD,
meanConversionToC = meanConversionToC,
degreeLost = degreeLost,
degreeLostC = degreeLostC,
degreeLostD = degreeLostD,
avg_wealth = avg_wealth,
gini = gini
))
#plot(g_r0,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", round 0",sep=""))
#plot(g_rX_final,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", final round",sep=""))
}
sum.netIntLowDegree <- data.frame(
df.netIntLowDegree %>%
group_by(coopFrac) %>%
summarise(
mean.isolation = mean(isolation),
ci.isolation = 1.96 * sd(isolation)/sqrt(n()),
mean.percentIsolation = mean(percentIsolation),
ci.percentIsolation = 1.96 * sd(percentIsolation)/sqrt(n()),
mean.percentIsolationC = mean(percentIsolationC,na.rm=TRUE),
ci.percentIsolationC = 1.96 * sd(percentIsolationC,na.rm=TRUE)/sqrt(sum(isolation)),
mean.percentIsolationD = mean(percentIsolationD,na.rm=TRUE),
ci.percentIsolationD = 1.96 * sd(percentIsolationD,na.rm=TRUE)/sqrt(sum(isolation)),
mean.avgCoop = mean(avgCoop,na.rm=TRUE),
ci.avgCoop = 1.96 * sd(avgCoop,na.rm=TRUE)/sqrt(n()),
mean.avgCoopFinal = mean(avgCoopFinal,na.rm=TRUE),
ci.avgCoopFinal = 1.96 * sd(avgCoopFinal,na.rm=TRUE)/sqrt(n()),
mean.nCommunities = mean(nCommunities,na.rm=TRUE),
ci.nCommunities = 1.96 * sd(nCommunities,na.rm=TRUE)/sqrt(n()),
mean.communitySize = mean(communitySize,na.rm=TRUE),
ci.communitySize = 1.96 * sd(communitySize,na.rm=TRUE)/sqrt(n()),
mean.assortativityInitial = mean(assortativityInitial,na.rm=TRUE),
ci.assortativityInitial = 1.96 * sd(assortativityInitial,na.rm=TRUE)/sqrt(n()),
mean.assortativityFinal = mean(assortativityFinal,na.rm=TRUE),
ci.assortativityFinal = 1.96 * sd(assortativityFinal,na.rm=TRUE)/sqrt(n()),
mean.conversionRate = mean(conversionRate,na.rm=TRUE),
ci.conversionRate = 1.96 * sd(conversionRate,na.rm=TRUE)/sqrt(n()),
mean.conversionToD = mean(conversionToD,na.rm=TRUE),
ci.conversionToD = 1.96 * sd(conversionToD,na.rm=TRUE)/sqrt(n()),
mean.conversionToC = mean(conversionToC,na.rm=TRUE),
ci.conversionToC = 1.96 * sd(conversionToC,na.rm=TRUE)/sqrt(n()),
mean.homophilyC = mean(homophilyC,na.rm=TRUE),
ci.homophilyC = 1.96 * sd(homophilyC,na.rm=TRUE)/sqrt(n()),
mean.homophilyD = mean(homophilyD,na.rm=TRUE),
ci.homophilyD = 1.96 * sd(homophilyD,na.rm=TRUE)/sqrt(n()),
mean.heterophily = mean(heterophily,na.rm=TRUE),
ci.heterophily = 1.96 * sd(heterophily,na.rm=TRUE)/sqrt(n()),
mean.transitivity = mean(transitivity,na.rm=TRUE),
ci.transitivity = 1.96 * sd(transitivity,na.rm=TRUE)/sqrt(n()),
mean.degree = mean(degree,na.rm=TRUE),
ci.degree = 1.96 * sd(degree,na.rm=TRUE)/sqrt(n()),
mean.degreeC = mean(degreeC,na.rm=TRUE),
ci.degreeC = 1.96 * sd(degreeC,na.rm=TRUE)/sqrt(n()),
mean.degreeD = mean(degreeD,na.rm=TRUE),
ci.degreeD = 1.96 * sd(degreeD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToD = mean(meanConversionToD,na.rm=TRUE),
ci.meanConversionToD = 1.96 * sd(meanConversionToD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToC = mean(meanConversionToC,na.rm=TRUE),
ci.meanConversionToC = 1.96 * sd(meanConversionToC,na.rm=TRUE)/sqrt(n()),
mean.degreeLost = mean(degreeLost,na.rm=TRUE),
ci.degreeLost = 1.96 * sd(degreeLost,na.rm=TRUE)/sqrt(n()),
mean.degreeLostC = mean(degreeLostC,na.rm=TRUE),
ci.degreeLostC = 1.96 * sd(degreeLostC,na.rm=TRUE)/sqrt(n()),
mean.degreeLostD = mean(degreeLostD,na.rm=TRUE),
ci.degreeLostD = 1.96 * sd(degreeLostD,na.rm=TRUE)/sqrt(n()),
mean.avg_wealth = mean(avg_wealth,na.rm=TRUE),
ci.avg_wealth = 1.96 * sd(avg_wealth,na.rm=TRUE)/sqrt(n()),
mean.gini = mean(gini,na.rm=TRUE),
ci.gini = 1.96 * sd(gini,na.rm=TRUE)/sqrt(n())
)
)
kable(sum.netIntLowDegree[c(1:9)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,10:17)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,18:25)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,26:33)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,34:ncol(sum.netIntLowDegree))]) %>% kableExtra::kable_styling(font_size = 10)
compare_means(percentIsolation ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoop ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoopFinal ~ coopFrac, data=df.netIntLowDegree)
compare_means(nCommunities ~ coopFrac, data=df.netIntLowDegree)
compare_means(communitySize ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityInitial ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityFinal ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionRate ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLost ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostD ~ coopFrac, data=df.netIntLowDegree)
summary(lm(percentIsolation ~ assortativityInitial, data=df.netIntLowDegree))
#plot(df.netIntLowDegree$assortativityInitial, df.netIntLowDegree$percentIsolation)
#percentIsolation
g.percentIsolation = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolation", add = "mean_se", color="coopFrac") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation when defectors are assigned to 25% of nodes by degree, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.0990, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0022, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0024, xend = 3.7, yend = -0.0024), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0022, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.10)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.percentIsolation)
#percentIsolationC
#percentage of isolation among those who cooperated in both practice rounds
g.percentIsolationC = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationC", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation among initial cooperators, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.0990, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0022, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0024, xend = 3.7, yend = -0.0024), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0022, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.10)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.percentIsolationC)
#percentIsolationD
#percentage of isolation among those who defected at least once in practice rounds
g.percentIsolationD = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationD", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.298, method="t.test", color="black") +
labs(
title = paste("Isolation among initial defectors, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.2990, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0062, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0064, xend = 3.7, yend = -0.0064), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0062, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.30)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.percentIsolationD)
#avgCoopFinal
g.avgCoopFinal = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avgCoopFinal", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Cooperation in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of cooperators in final round") +
annotate("text", x=1, y=0.990, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0212, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0214, xend = 3.7, yend = -0.0214), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0212, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,1.0)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.avgCoopFinal)
#avg_wealth
g.avg_wealth = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avg_wealth", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 6800, method="t.test", color="black") +
labs(
title = paste("Wealth in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Average wealth in final round") +
annotate("text", x=1, y=6900, label= "ref", color="black") +
annotate("text", x=2.4, y= -162, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -164, xend = 3.7, yend = -164), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -162, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,7000)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.avg_wealth)
#gini
g.gini = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="gini", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.48, method="t.test", color="black") +
labs(
title = paste("Gini coefficient in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Gini coefficient in final round") +
annotate("text", x=1, y=0.490, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0112, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0114, xend = 3.7, yend = -0.0114), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0112, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.50)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.gini)
#degree
g.degree = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="degree", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 14.8, method="t.test", color="black") +
labs(
title = paste("Degree in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Mean degree in final round") +
annotate("text", x=1, y=14.90, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.312, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.314, xend = 3.7, yend = -0.314), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.312, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,15)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.degree)
#transitivity
g.transitivity = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="transitivity", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Transitivity in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Transitivity in final round") +
annotate("text", x=1, y=0.99, label= "ref", color="black") +
annotate("text", x=2.4, y= -0.0212, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 3.3, y = -0.0214, xend = 3.7, yend = -0.0214), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=4.6, y= -0.0212, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,1.00)) +
scale_x_discrete(labels=c('Control','0-25','25-50','50-75','75-100')) +
scale_color_manual(values = c('0' = "black",'0.25'="black",'0.5'="black",'0.75'="black",'1'="black")) +
geom_vline(xintercept = 1.5, linetype = "longdash")
print(g.transitivity)
#initial C-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyC, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("C-assortativity, ","Control",sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
else{
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyC, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("C-assortativity, degree %ile = ",key,sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
lapply(unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
reg = lm(percentIsolation ~ homophilyC + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, ","Control"," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
else{
reg = lm(percentIsolation ~ homophilyC + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, degree %ile = ",key," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
}
)
#initial D-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyD, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("D-assortativity, ","Control",sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
else{
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyD, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("D-assortativity, degree %ile = ",key,sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
lapply(unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
reg = lm(percentIsolation ~ homophilyD + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, ","Control"," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
else{
reg = lm(percentIsolation ~ homophilyD + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, degree %ile = ",key," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
}
)
}
}
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08381220 0.025045426 3.3464073 0.001165759
## homophilyC -0.04135387 0.044228442 -0.9350062 0.352106970
## degreeD -0.01039184 0.004098419 -2.5355728 0.012824166
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08985512 0.025657306 3.5021260 0.0007023639
## homophilyC 0.02742732 0.063796287 0.4299203 0.6682171102
## degreeD -0.01631013 0.005867373 -2.7798017 0.0065453206
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06388076 0.01952778 3.271277 0.001483225
## homophilyC 0.04882743 0.04587724 1.064306 0.289832271
## degreeD -0.01499927 0.00450415 -3.330101 0.001228700
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0069891020 0.016817371 0.4155883 0.6786292
## homophilyC -0.0047525923 0.035790689 -0.1327885 0.8946357
## degreeD 0.0009484012 0.003713231 0.2554113 0.7989462
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0054668772 0.016781546 0.3257672 0.7453022
## homophilyC -0.0067801091 0.034521450 -0.1964028 0.8447058
## degreeD 0.0008799064 0.002786165 0.3158128 0.7528231
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.068433706 0.020291313 3.3725618 0.001071087
## homophilyD -0.009680311 0.034427062 -0.2811832 0.779168840
## degreeD -0.009284215 0.005047849 -1.8392417 0.068938600
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10012895 0.020452024 4.895797 3.952479e-06
## homophilyD -0.03436047 0.032089764 -1.070761 2.869616e-01
## degreeD -0.01390377 0.006055707 -2.295978 2.385242e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.071390731 0.018370955 3.88606524 0.000186344
## homophilyD 0.000559646 0.027751112 0.02016661 0.983951885
## degreeD -0.013327779 0.004498184 -2.96292455 0.003833145
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0084326126 0.017148501 0.4917405 0.6240253
## homophilyD -0.0097024449 0.014669762 -0.6613908 0.5099461
## degreeD 0.0009904694 0.003329289 0.2975018 0.7667263
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0045936362 0.016188758 0.28375469 0.7772032
## homophilyD -0.0006723957 0.014993911 -0.04484458 0.9643234
## degreeD 0.0007195459 0.002707466 0.26576356 0.7909853
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.033605788 0.011842923 2.837626 0.005534875
## homophilyC -0.012119781 0.022435271 -0.540211 0.590289698
## degreeD -0.005552269 0.001971913 -2.815677 0.005896180
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09662072 0.018287572 5.2834086 7.788142e-07
## homophilyC 0.02919679 0.053083677 0.5500145 5.835737e-01
## degreeD -0.02415924 0.004641811 -5.2047009 1.085854e-06
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.011440441 0.01216015 0.9408142 0.3491372
## homophilyC 0.020714425 0.03493515 0.5929394 0.5546016
## degreeD -0.002260312 0.00308758 -0.7320659 0.4658934
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019027958 0.010114935 1.8811745 0.0629473
## homophilyC -0.030078123 0.028446023 -1.0573753 0.2929665
## degreeD -0.001206957 0.002445782 -0.4934851 0.6227853
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.014174234 0.011579468 1.224083 0.22391643
## homophilyC 0.031403378 0.022714249 1.382541 0.17001352
## degreeD -0.003468453 0.001787142 -1.940782 0.05521811
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0296089615 0.009571529 3.09344112 0.002584603
## homophilyD -0.0002609031 0.014632270 -0.01783066 0.985810568
## degreeD -0.0054672212 0.002389313 -2.28819813 0.024296723
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09938802 0.01546698 6.425820 4.898207e-09
## homophilyD 0.02634872 0.02587569 1.018281 3.110776e-01
## degreeD -0.02467710 0.00451236 -5.468780 3.526463e-07
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0152267766 0.011478688 1.326526 0.1877802
## homophilyD -0.0153228560 0.014011876 -1.093562 0.2768554
## degreeD -0.0004989438 0.002743575 -0.181859 0.8560729
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019438986 0.010193031 1.9070859 0.05946832
## homophilyD -0.012298125 0.015607167 -0.7879793 0.43263019
## degreeD -0.002295946 0.002020572 -1.1362851 0.25863676
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.020355236 0.010846043 1.8767431 0.06362248
## homophilyD -0.001490668 0.012025724 -0.1239566 0.90161151
## degreeD -0.002901979 0.001890429 -1.5350907 0.12808540
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.040043029 0.024409221 1.6404878 0.10414275
## homophilyC 0.042830009 0.046240907 0.9262364 0.35662195
## degreeD -0.006977379 0.004064272 -1.7167600 0.08921469
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09377698 0.01983889 4.7269277 7.718757e-06
## homophilyC -0.02645031 0.05758671 -0.4593128 6.470372e-01
## degreeD -0.01710021 0.00503557 -3.3958829 9.928095e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.070639588 0.017735064 3.9830467 0.0001315937
## homophilyC 0.008661867 0.050951436 0.1700024 0.8653622976
## degreeD -0.012674395 0.004503105 -2.8145903 0.0059146109
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07219528 0.020715943 3.4850103 0.0007404981
## homophilyC 0.05135605 0.058259019 0.8815125 0.3802195955
## degreeD -0.01363021 0.005009097 -2.7210911 0.0077131977
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.043785810 0.017515660 2.4998093 0.01412229
## homophilyC -0.062973888 0.034358664 -1.8328387 0.06992605
## degreeD -0.002637588 0.002703317 -0.9756858 0.33167241
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.053739920 0.019784616 2.71624776 0.0078187
## homophilyD -0.001939844 0.030245308 -0.06413701 0.9489931
## degreeD -0.007013965 0.004938776 -1.42018277 0.1587607
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08849382 0.016859740 5.24882459 9.015462e-07
## homophilyD 0.00269032 0.028205731 0.09538203 9.242082e-01
## degreeD -0.01815127 0.004918686 -3.69026742 3.699165e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07313720 0.016759295 4.3639783 3.191422e-05
## homophilyD -0.01660695 0.020457840 -0.8117646 4.189143e-01
## degreeD -0.01139078 0.004005717 -2.8436296 5.439650e-03
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073532110 0.020900656 3.5181723 0.0006631166
## homophilyD -0.007243491 0.032002260 -0.2263431 0.8214108533
## degreeD -0.010504132 0.004143153 -2.5352989 0.0128335594
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.031594956 0.01651168 1.9134913 0.05869598
## homophilyD 0.003001154 0.01830759 0.1639295 0.87013491
## degreeD -0.003783143 0.00287793 -1.3145361 0.19183068
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.025898420 0.013626718 1.9005617 0.06032866
## homophilyC 0.013136450 0.025814499 0.5088787 0.61199280
## degreeD -0.005161841 0.002268925 -2.2750163 0.02510644
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06718219 0.018324481 3.666253 4.017404e-04
## homophilyC 0.10687813 0.053190814 2.009334 4.728010e-02
## degreeD -0.02240514 0.004651179 -4.817089 5.372374e-06
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.017155895 0.010691149 1.604682 0.11181487
## homophilyC 0.051648800 0.030714826 1.681559 0.09587138
## degreeD -0.006269121 0.002714586 -2.309420 0.02304134
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.018299905 0.010131912 1.8061649 0.0739938
## homophilyC -0.028434926 0.028493768 -0.9979349 0.3207939
## degreeD -0.001163511 0.002449887 -0.4749242 0.6359086
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004824486 0.010656973 0.452707 0.65178119
## homophilyC 0.036081826 0.020904686 1.726016 0.08756164
## degreeD -0.002244452 0.001644767 -1.364602 0.17556830
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0302981575 0.011011253 2.75156300 0.007078624
## homophilyD 0.0007356138 0.016833218 0.04370013 0.965233270
## degreeD -0.0052957900 0.002748707 -1.92664763 0.056950462
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0874530721 0.015876988 5.50816514 2.975118e-07
## homophilyD -0.0005680645 0.026561623 -0.02138666 9.829811e-01
## degreeD -0.0187477938 0.004631977 -4.04747166 1.041346e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.023647935 0.010272853 2.3019832 0.02347462
## homophilyD -0.005312024 0.012539930 -0.4236088 0.67278899
## degreeD -0.003641735 0.002455362 -1.4831760 0.14126959
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019109594 0.010170697 1.8788873 0.06326244
## homophilyD -0.017460066 0.015572970 -1.1211776 0.26498026
## degreeD -0.001931346 0.002016145 -0.9579397 0.34047470
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.011801337 0.010027932 1.1768466 0.2421970
## homophilyD -0.004770760 0.011118630 -0.4290780 0.6688379
## degreeD -0.001429734 0.001747835 -0.8180031 0.4154024
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.051526128 0.025605547 2.0123033 0.04696068
## homophilyC 0.012823161 0.048507231 0.2643557 0.79206674
## degreeD -0.006984486 0.004263467 -1.6382175 0.10461626
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13245398 0.020528613 6.452164 4.337680e-09
## homophilyC -0.06401073 0.059588787 -1.074208 2.853945e-01
## degreeD -0.02385153 0.005210639 -4.577468 1.395673e-05
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.072363084 0.017527091 4.128642 7.728781e-05
## homophilyC -0.064554004 0.050353945 -1.282005 2.028966e-01
## degreeD -0.008030592 0.004450298 -1.804506 7.425517e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05850039 0.019033961 3.073474 0.002747279
## homophilyC 0.10457035 0.053528815 1.953534 0.053637287
## degreeD -0.01431963 0.004602395 -3.111343 0.002446403
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065460234 0.018197353 3.597239 0.0005105224
## homophilyC -0.092079305 0.035695871 -2.579551 0.0114096538
## degreeD -0.004527012 0.002808528 -1.611881 0.1102708252
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.055360051 0.02067039 2.67822946 0.008693535
## homophilyD -0.002367525 0.03159942 -0.07492305 0.940430241
## degreeD -0.006830638 0.00515989 -1.32379539 0.188682337
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11887514 0.017510221 6.7889001 9.039512e-10
## homophilyD 0.01410119 0.029293962 0.4813686 6.313387e-01
## degreeD -0.02682959 0.005108458 -5.2519931 8.895573e-07
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06574369 0.016735830 3.9283199 0.0001602421
## homophilyD -0.01003535 0.020429196 -0.4912259 0.6243763303
## degreeD -0.01042007 0.004000109 -2.6049456 0.0106345944
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.060070288 0.019504833 3.07976430 0.002695035
## homophilyD 0.001211793 0.029865030 0.04057564 0.967717600
## degreeD -0.008670251 0.003866458 -2.24242720 0.027210904
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.047643234 0.017426109 2.7340144 0.007463855
## homophilyD 0.005606716 0.019321478 0.2901805 0.772310631
## degreeD -0.006265482 0.003037312 -2.0628377 0.041856963
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.


## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing non-finite values (`stat_compare_means()`).






## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.031971422 0.014295098 2.2365304 0.02760779
## homophilyC 0.003092835 0.027080680 0.1142082 0.90930885
## degreeD -0.005307060 0.002380214 -2.2296569 0.02807680
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06801968 0.017117696 3.9736472 0.0001361381
## homophilyC -0.03753618 0.049687855 -0.7554397 0.4518156846
## degreeD -0.01094191 0.004344869 -2.5183520 0.0134269817
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.026350935 0.012417351 2.1221061 0.03637625
## homophilyC -0.008693146 0.035674066 -0.2436825 0.80799135
## degreeD -0.003488598 0.003152886 -1.1064779 0.27125629
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.021226558 0.01118312 1.898088 0.06065756
## homophilyC 0.034699119 0.03145007 1.103308 0.27262305
## degreeD -0.005128568 0.00270407 -1.896611 0.06085474
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0090313329 0.013308605 0.67860853 0.4990186
## homophilyC -0.0007183182 0.026106118 -0.02751532 0.9781058
## degreeD -0.0006736654 0.002054012 -0.32797534 0.7436445
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing non-finite values (`stat_cor()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).


## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.037000802 0.011397568 3.246377 0.001605173
## homophilyD 0.026909388 0.017423788 1.544405 0.125746559
## degreeD -0.007804631 0.002845142 -2.743143 0.007249028
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.25 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05917541 0.014539458 4.0699873 9.590382e-05
## homophilyD 0.01670337 0.024323983 0.6867039 4.939077e-01
## degreeD -0.01317100 0.004241763 -3.1050774 2.493970e-03
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.5 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.026035314 0.011758029 2.2142584 0.02915289
## homophilyD -0.007774346 0.014352863 -0.5416582 0.58929603
## degreeD -0.003465859 0.002810341 -1.2332521 0.22046194
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 0.75 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.022781138 0.011273484 2.0207718 0.04605972
## homophilyD -0.013917220 0.017261513 -0.8062572 0.42206684
## degreeD -0.002611699 0.002234751 -1.1686754 0.24539859
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.009537272 0.012251261 0.7784727 0.4382251
## homophilyD 0.016036137 0.013583782 1.1805355 0.2407347
## degreeD -0.001543735 0.002135353 -0.7229410 0.4714923
plot.trends <-
data.frame(
trends.df %>%
group_by(round, V, GINI, fractionCoop) %>%
summarize_all(list(mean=~mean(., na.rm=TRUE),sd=~sd(., na.rm=TRUE)))
)
plot.trends$V = factor(plot.trends$V)
plot.trends$GINI = factor(plot.trends$GINI)
for(i in unique(plot.trends$fractionCoop)){
g.gini = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gini_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gini_mean - gini_sd, ymax = gini_mean + gini_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gini") +
theme_bw()
g.gmd = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gmd_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gmd_mean - gmd_sd, ymax = gmd_mean + gmd_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gmd") +
theme_bw()
g.avg_wealth = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_wealth_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_wealth_mean - avg_wealth_sd, ymax = avg_wealth_mean + avg_wealth_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_wealth") +
theme_bw()
g.avg_coop = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_coop_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_coop_mean - avg_coop_sd, ymax = avg_coop_mean + avg_coop_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_coop") +
theme_bw()
g.avg_degree = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_degree_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_degree_mean - avg_degree_sd, ymax = avg_degree_mean + avg_degree_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_degree") +
theme_bw()
plot <- ggarrange(g.gini,g.gmd,g.avg_wealth,g.avg_coop,g.avg_degree,common.legend = TRUE,legend="bottom")
print(annotate_figure(plot, top = text_grob(paste("Degree percentile of nodes assigned to defectors =",i), color = "black", face = "bold", size = 10)))
}
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).


fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyC, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = heterophily, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyC, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig5 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig6 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = heterophily, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig7 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = degreeD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Mean degree of defectors") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
print(ggarrange(fig1,fig2,fig3,fig4,fig5,fig6,fig7,common.legend = TRUE,legend="right"))
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).

fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Isolated individuals (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Isolated individuals (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = homophilyC, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("C-assortativity") +
scale_y_continuous("Isolated individuals (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = homophilyD, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("D-assortativity") +
scale_y_continuous("Isolated individuals (%)")
print(ggarrange(fig1,fig2,fig3,fig4,common.legend = TRUE,legend="right"))
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).

reg.isolation = glm(percentIsolation*100 ~ degreeC + degreeD + homophilyC + homophilyD + heterophily, data=df.netIntLowDegree, family = gaussian(link = "identity"))
summary(reg.isolation)
##
## Call:
## glm(formula = percentIsolation * 100 ~ degreeC + degreeD + homophilyC +
## homophilyD + heterophily, family = gaussian(link = "identity"),
## data = df.netIntLowDegree)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2692 -1.1750 -0.7111 -0.1296 10.7908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.365628 0.721194 4.667 3.95e-06 ***
## degreeC 0.215958 0.203317 1.062 0.2887
## degreeD -0.355943 0.146492 -2.430 0.0155 *
## homophilyC -1.890118 2.100683 -0.900 0.3687
## homophilyD 0.006919 0.886176 0.008 0.9938
## heterophily -4.219492 3.120830 -1.352 0.1770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.718618)
##
## Null deviance: 2505.2 on 497 degrees of freedom
## Residual deviance: 2321.6 on 492 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 2193.9
##
## Number of Fisher Scoring iterations: 2
#variance inflation factor
car::vif(reg.isolation)
## degreeC degreeD homophilyC homophilyD heterophily
## 4.233107 3.189170 2.266295 1.479627 3.267568
reg.isolation = glm(percentIsolation*100 ~ degreeD + homophilyD, data=df.netIntLowDegree, family = gaussian(link = "identity"))
summary(reg.isolation)
##
## Call:
## glm(formula = percentIsolation * 100 ~ degreeD + homophilyD,
## family = gaussian(link = "identity"), data = df.netIntLowDegree)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1186 -1.1639 -0.7090 -0.1524 11.0536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.15124 0.42259 7.457 3.99e-13 ***
## degreeD -0.50925 0.08399 -6.064 2.65e-09 ***
## homophilyD 0.53079 0.74589 0.712 0.477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.7076)
##
## Null deviance: 2505.2 on 497 degrees of freedom
## Residual deviance: 2330.3 on 495 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 2189.7
##
## Number of Fisher Scoring iterations: 2
#variance inflation factor
car::vif(reg.isolation)
## degreeD homophilyD
## 1.050707 1.050707
#double machine learning
library(DoubleML)
library(mlr3)
library(mlr3learners)
set.seed(3141)
##degreeC
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "degreeC",
x_cols = c("degreeD","homophilyC","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeC
## Covariates: degreeD, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeC
## Covariates: degreeD, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## degreeC 0.002634 0.001680 1.568 0.117
##degreeD
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "degreeD",
x_cols = c("degreeC","homophilyC","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeD
## Covariates: degreeC, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeD
## Covariates: degreeC, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## degreeD -0.003354 0.001277 -2.626 0.00864 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##homophilyC
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "homophilyC",
x_cols = c("degreeC","degreeD","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyC
## Covariates: degreeC, degreeD, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyC
## Covariates: degreeC, degreeD, homophilyD, heterophily
## Instrument(s):
## No. Observations: 498
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## homophilyC -0.01779 0.01851 -0.961 0.336
##homophilyD
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "homophilyD",
x_cols = c("degreeC","degreeD","homophilyC","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyD
## Covariates: degreeC, degreeD, homophilyC, heterophily
## Instrument(s):
## No. Observations: 498
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyD
## Covariates: degreeC, degreeD, homophilyC, heterophily
## Instrument(s):
## No. Observations: 498
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## homophilyD 0.007204 0.007472 0.964 0.335
##heterophily
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "heterophily",
x_cols = c("degreeC","degreeD","homophilyC","homophilyD"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): heterophily
## Covariates: degreeC, degreeD, homophilyC, homophilyD
## Instrument(s):
## No. Observations: 498
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): heterophily
## Covariates: degreeC, degreeD, homophilyC, homophilyD
## Instrument(s):
## No. Observations: 498
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## heterophily -0.03833 0.02826 -1.356 0.175
fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyC, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = heterophily, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyC, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig5 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig6 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = heterophily, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig7 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = degreeD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Mean degree of defectors") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
print(ggarrange(fig1,fig2,fig3,fig4,fig5,fig6,fig7,common.legend = TRUE,legend="right"))
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Removed 1 rows containing missing values (`geom_point()`).
